Robin Schmucker

I am a fourth year Ph.D. student in the Machine Learning Department at Carnegie Mellon University, where I am very fortunate to be advised by Professor Tom Mitchell.

Previously, I spent three wonderful years at the Karlsruhe Institute of Technology, completing my bachelor's degree in Computer Science. I was a research assistant and part of the TECO research group lead by Professor Michael Beigl. I am grateful for the mentorship of Professor Manuela Veloso who hosted me as a research intern as part of the CLICS scholarship program.

In the industry, I worked as a research intern at AWS where I designed new algorithms and contributed to the AutoGluon project.

Email  /  CV  /  LinkedIn  /  Google Scholar  /  GitHub

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My research interests are broadly in the areas of machine learning and optimization. Particularly, I am interested in the theoretical and practical problems that arise when deploying data driven techniques in real world settings such as education and healthcare. Currently, my research focuses on problems related to intelligent tutoring systems including deep learning for student performance predictions and reinforcement learning for personalized curriculum design.

Transferable Student Performance Modeling for Intelligent Tutoring Systems
Robin Schmucker, Tom M. Mitchell
Accepted at ICCE, 2022

We propose transfer learning techniques that can mitigate the student performance modeling cold-start problem for new courses by leveraging log data from existing courses. Our course-agnostic models enable accurate predictions for future courses when they are first deployed.

Assessing the Performance of Online Students - New Data, New Approaches, Improved Accuracy
Robin Schmucker, Jingbo Wang, Shijia Hu, Tom M. Mitchell
Journal of Educational Data Mining, 2022
Video, GitHub

We study how to utilize various types of student log data for performance modeling using four recent large-scale datasets. We propose various extensions over earlier methods and define a new state of the art for logistic regression-based performance modeling.

Combination Treatment Optimization Using a Pan-Cancer Pathway Model
Robin Schmucker, Gabriele Farina, James Faeder, Fabian Fröhlich, Ali Sinan Saglam, Tuomas Sandholm
PLOS Computational Biology, 2021

We use a pan-cancer pathway model to identify novel combination therapies by defining multiple treatment optimization problems and solving them by combining CMA-ES with an efficient Hamiltonian Monte-Carlo sampling scheme. We also consider sequential treatment plans.

Multi-objective Asynchronous Successive Halving
Robin Schmucker, Michele Donini, Muhammad Bilal Zafar, David Salinas, Cédric Archambeau
arXiv, 2021

We propose multiple algorithms that extend ASHA to the multi-objective hyperparameter optimization (HPO) setting. We assess the performance of our methods on various real world tasks related to neural architecture search, algorithmic fairness, and language model optimization.

Fair Bayesian Optimization
Valerio Perrone, Michele Donini, Muhammad Bilal Zafar, Robin Schmucker, Krishnaram Kenthapadi, Cédric Archambeau
AAAI/ACM Conference on AI, Ethics, and Society (AIES), 2021

We introduce a general constrained Bayesian optimization framework to optimize the performance of any ML model while enforcing different fairness constraints. Our approach is competitive with techniques that enforce model-specific constraints and ones that learn fair representations ahead of time.

Bandit Linear Optimization for Sequential Decision Making and Extensive-Form Games
Gabriele Farina, Robin Schmucker, Tuomas Sandholm
AAAI Conference on Artificial Intelligence, 2021

We propose the first algorithm for the bandit linear optimization problem for tree-form sequential decision making that offers both (i) linear-time iterations (in the size of the decision tree) and (ii) O(√T) cumulative regret in expectation compared to any fixed strategy, at all times T.

Multi-Objective Multi-Fidelity Hyperparameter Optimization with Application to Fairness
Robin Schmucker, Michele Donini, Valerio Perrone, Muhammad Bilal Zafar, Cédric Archambeau
NeurIPS Workshop on Meta-Learning, 2020

We study the suitability of existing multi-objective algorithms for ML hyperparameter optimization and propose a novel multi-fidelity method. We evaluate on multiple fairness-motivated applications and achieve lower wall-clock times when approximating Pareto frontiers.

Counterfactual-Free Regret Minimization for Sequential Decision Making and Extensive-Form Games
Gabriele Farina, Robin Schmucker, Tuomas Sandholm
AAAI Workshop on Reinforcement Learning in Games, 2020

We propose the first efficient regret minimization algorithm for the bandit linear optimization problem on sequential decision processes and extensive-form games and show that it achieves O(√T) cumulative regret in expectation against any strategy.

Towards a Robust Interactive and Learning Social Robot
Michiel de Jong, Kevin Zhang, Aaron M. Roth, Travers Rhodes, Robin Schmucker, Chenghui Zhou, Sofia Ferreira, João Cartucho, Manuela Veloso
Conference on Autonomous Agents and Multiagent Systems (AAMAS), 2018

We augment Pepper's perception by integrating state-of-the-art vision and speech recognition systems. As we recognize limitations of the individual perceptual modalities, we introduce a multi-modality approach to increase the robustness of human social interaction with the robot. We combine vision, gesture, speech, and input from an onboard tablet, a remote mobile phone, and external microphones.

Multimodal Movement Activity Recognition Using a Robot’s Proprioceptive Sensors
Robin Schmucker, Chenghui Zhou, Manuela Veloso
RoboCup 2018: Robot World Cup XXII, 2018

By introducing Human Activity Recognition approaches to the robotics domain, we aim at creating agents that can detect their own body’s activities. Our activity recognition pipeline can detect unexpected behavior and can be used to extend Pepper’s inbuilt capabilities.

A universal test for gravitational decoherence
C. Pfister, J. Kaniewski, M. Tomamichel, A. Mantri, R. Schmucker, N. McMahon, G. Milburn, S. Wehner
Nature Communications, 2016

Quantum mechanics (QM) and the theory of gravity are presently not compatible. One question is whether gravity causes decoherence. We propose a method to estimate gravitational decoherence in an experiment that can draw conclusions in any physical theory where the no-signalling principle holds, even if QM needs to be modified.


10-403: Deep Reinforcement Learning & Control

Teaching Assistant in Spring 2022.

convex optimization
10-725: Convex Optimization

Teaching Assistant in Fall 2020.

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